Methods, apparatus, and articles of manufacture to measure geographical features using an image of a geographical location

ABSTRACT

Methods, apparatus, and articles of manufacture to measure geographical features using an image of a geographical location are disclosed. An example method includes identifying first features of a first open market from a first image of a first geographic area, identifying second features of a second image of a second geographic area, comparing the first features to the second features, and identifying a second open market in the second image based on the comparison.

RELATED APPLICATIONS

This patent claims priority to U.S. Provisional Patent Application Ser.No. 61/644,850, filed May 9, 2012, the entirety of which is herebyincorporated by reference.

FIELD OF THE DISCLOSURE

This disclosure relates generally to image analysis, and, moreparticularly, to methods, apparatus, and articles of manufacture tomeasure geographical features using an image of a geographical location.

BACKGROUND

Manufacturers of goods sometimes wish to measure the market presence oftheir goods in particular markets to determine markets in which they areunder-represented and/or over-represented. Smaller, growing markets areoften desirable targets for such studies. As these markets grow largerand/or mature, previous market research becomes obsolete and may beupdated and/or performed again.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example system to generate samplingareas using images of a geographic area of interest constructed inaccordance with the teachings of this disclosure.

FIG. 2 is a more detailed block diagram of the example sampling areagenerator of FIG. 1.

FIGS. 3A-3E illustrate an example satellite image of a geographic areaof interest undergoing an example process to generate sampling areas.

FIG. 4 is a flowchart representative of example machine readableinstructions which may be executed to implement the example samplingarea generator to measure geographical features using an image of ageographical location.

FIG. 5 is a flowchart representative of example machine readableinstructions which may be executed to implement the example samplingarea generator of FIGS. 1 and/or 2 to generate sampling areas based onmeasured geographical features.

FIG. 6 is a flowchart representative of example machine readableinstructions which may be executed to implement the example zoneanalyzer of FIG. 2 to locate open markets.

FIG. 7 is a flowchart representative of example machine readableinstructions which may be executed to implement the example zoneanalyzer of FIG. 2 to analyze features an image of a geographicallocation.

FIG. 8 is a flowchart representative of example machine readableinstructions which may be executed to implement the example zoneanalyzer of FIG. 2 to locate open markets that are in unenumeratedareas.

FIG. 9 is a flowchart representative of example machine readableinstructions which may be executed to implement the example zoneanalyzer of FIG. 2 to use data representative of located open markets torank open markets.

FIG. 10 is a flowchart representative of example machine readableinstructions which may be executed to implement the example zoneanalyzer of FIG. 2 to use data representative of located open markets topredict a number of stores in an open market.

FIG. 11 is a flowchart representative of example machine readableinstructions which may be executed to implement the example zoneanalyzer of FIG. 2 to use data representative of located open markets toproject a growth rate of an open market.

FIG. 12 is a flowchart representative of example machine readableinstructions which may be executed to implement the example zoneanalyzer of FIG. 2 to use located open markets to modify sampling areas.

FIG. 13 is a flowchart representative of example machine readableinstructions which may be executed to implement the example zoneanalyzer of FIG. 2 to use located open markets to estimate a totalnumber of open markets in a geographic area.

FIG. 14 is a flowchart representative of example machine readableinstructions which may be executed to implement the example zoneanalyzer of FIG. 2 to use data representative of located open markets toestimate a trade area of an open market.

FIG. 15 is a block diagram of an example processor platform capable ofexecuting the instructions of FIGS. 4-14 to implement the sampling areagenerator and/or the zone analyzer of FIGS. 1 and 2.

DETAILED DESCRIPTION

Retail Measurement Services (RMS) collect point of sale (POS) and/orother statistics representing sales, inventory, and/or other data aboutretail stores. The measured retail stores are representative of retailcategories to be measured. Known methods of retail measurement includesampling, which avoids the costly efforts involved in enumerating orcounting all retail stores in an area being measured. Known samplingmethods include drawing sampling areas of a retail market using a localmap, taking samples from each area or a subset of these areas, andestimating the retail coverage based on the samples. A RetailEnumeration Survey (RES) is performed prior to producing the RMS data.The RES generates estimates of the number of stores by type, bycategories of targeted goods that are carried, and by region.

Due to the high rate of economic, social and/or population growth insome emerging economies, and lack of current and/or accurate maps of thelocations measured, sampling and coverage of areas can be incomplete. Inaddition, a lack of current and/or accurate maps impacts samplingdecisions (e.g., by forcing assumptions to be made when determiningsampling strategies) which are different between geographic areas (e.g.,different countries, cultures, market considerations, etc.). Knowntechniques of sampling in such conditions can miss significant retailvolumes, markets, and/or potential markets present in geographic areas,thereby potentially resulting in inaccurate and/or incomplete retailmeasurements. Inefficient sampling can add significant cost to ameasurement, as sample stratification by an expected number of stores inan area is a difficult challenge. Product marketers desire accurate,consistent, and efficient total coverage of their markets.

Example methods, apparatus, and/or articles of manufacture disclosedherein improve accuracy of sampling, data quality, weighting, andcoverage of RES. Example methods, apparatus, and/or articles ofmanufacture enable standardization of sampling and/or reporting ofretail categories by dividing a geographical area of interest intogeographic zones having approximately the same size (e.g., standardizedsizes). In some such examples, these geographical zones are used asareas in which a discrete sampling activity of one or more retail storesis to take place.

Example methods, apparatus, and/or articles of manufacture disclosedherein use geographical zones for sampling and reporting media and/orretail consumption. In some such examples, digital maps and/or satelliteimages are used to measure geographic features of an area of interestsuch as location, development, and/or land use. In some examples, thegeographical zones are equal or substantially equal in area (e.g., eachgeographical zone represents an equal or substantially equalgeographical area).

In some examples, the satellite images are used to determine and/oranalyze geographical features such as street density, land use, etc.,and/or image features such as pixel weight, etc., for each geographicalzone in an image of an area of interest. Based on the measurement of thegeographical feature and/or image feature, example methods, apparatus,and/or articles of manufacture generate sampling areas to be used toestablish various universe estimates for an RES study. Samplingoccurring based on these sampling areas may advantageously result insubstantially full representation of retail markets within ageographical area of interest and/or in reporting consistent data acrossdifferent geographical areas. In some examples, the sampling areasspecify or approximate a number of geographical zones to be sampled(e.g., in the case of sampling retail stores for a brand owner,specifying geographical zones to be sampled for retail stores,inventory, product availability, etc.).

In some example methods, apparatus, and/or articles of manufacturedisclosed herein, a description of the geographical zones is stored in amemory. In some such examples, measurement(s) of geographical featuresand/or image features associated with the geographical zones are storedin association with the descriptions of the respective geographicalzones. Some example methods, apparatus, and/or articles of manufacturedisclosed herein generate sampling areas for the geographic area(s) ofinterest based on the descriptions and the measurements. In someexamples, a number of geographical zones within the geographic area(s)of interest are selected to be sampled and the descriptions of theselected geographical zones are provided to a sampling entity which isto perform sampling.

Example methods and apparatus disclosed herein are useful in marketestimation in the developing world, where data from people on the groundin the area can be sparse. Example methods and apparatus extract textureand/or color features from aerial images that are characteristic of openmarkets. Example methods and apparatus disclosed herein assemble theextracted features in a scale-invariant feature transform. Examplemethods and apparatus use the features and aerial images to identifyopen markets in the same geographic area and/or to identify open marketsin geographically similar areas. Example methods and apparatus disclosedherein additionally or alternative identify open markets in unenumeratedareas (e.g., white space), create ranked lists of open markets accordingto designated criteria (e.g., size), reduce data acquisition costs andtime in identifying open markets by predicting sizes, numbers of stores,and/or other characteristics of open markets without sending people tophysically visit the area(s) under analysis.

Example methods and apparatus disclosed herein use the area of an openmarket to estimate a market channel or other commercially valuable data,rather than enumerating such data via sampling or surveyed. Examplemethods and apparatus disclosed herein project open market growth ratesover time, improve sampling methodology to ensure sampling areas includea statistically satisfactorily representative number of open markets,and/or create more accurate universe estimations for open markets basedon identifying all or a substantial portion of open markets in ageographic area. Example methods and apparatus disclosed herein estimatethe trade (e.g., retail) areas of open markets (e.g., based on theproximity to other open markets).

FIG. 1 is a block diagram of an example system 100 to generate samplingareas using one or more images of a geographic area of interest. Theexample system 100 of FIG. 1 may be used to identify geographical areasto be sampled for an RES study such as a study of retail locations,product availability, and/or any other type of population, data, ormarket of interest.

As illustrated in FIG. 1, the example system 100 includes a samplingarea generator 102, a sampling area requester 104, and a geographic datarepository 106. The sampling area generator 102, the sampling arearequester 104, and the geographic data repository 106 arecommunicatively coupled via a network 108. The example network 108 maybe a wide-area network such as the Internet.

The example sampling area generator 102 of FIG. 1 receives a request foran identification of one or more sampling areas for a geographic area orregion of interest. For example, the sampling area generator 102 mayreceive a request communicated from the sampling area requester 104 viathe network 108. The sampling area generator 102 responds to such arequest by retrieving one or more up-to-date (e.g., current or mostrecent) image(s) of the specified geographic area of interest from thegeographic data repository 106. Example satellite and/or aerial imagerepositories that may be employed to implement the geographic datarepository 106 are available from DigitalGlobe®, GeoEye®, RapidEye, SpotImage®, and/or the U.S. National Aerial Photography Program (NAPP). Theexample geographic data repository 106 may additionally or alternativelyinclude geographic data such as digital map representations, source(s)of population information, and/or external source(s) for parks, roadclassification, bodies of water, etc.

The example sampling area generator 102 of FIG. 1 receives the requestedimage(s) covering the requested area and identifies a number ofgeographical zones within the area. The example sampling area generator102 of FIG. 1 generates the geographical zones such that each of thezones is approximately the same size (e.g., covers the same amount ofarea as the other zones). In some examples, the sampling area generator102 uses the geographical zones as sampling areas. In other examples,the sampling area generator 102 further sub-divides the geographicalzones as discussed in more detail below. The example sampling areagenerator 102 of FIG. 1 further determines which geographical zones areto be used as sampling areas.

The sampling area generator 102 of the illustrated example outputs thegeographical zones (e.g., outputs identifying descriptions of thegeographical zones). In some examples, the geographical zones are outputin conjunction with an identification of those geographical zones thatare to be sampled (e.g., by a sampler or enumerator). In the illustratedexample, the sampling area generator 102 provides the sampling areas tothe sampling area requester 104. The sampling area requester 104 may be,for example, a client requesting sampling areas and/or a sampling entitythat intends to use the sampling areas to perform sampling services. Anexample sampling entity may be The Nielsen Company (U.S), LLC, or one ofits related entities.

FIG. 2 is a more detailed block diagram of the example sampling areagenerator 102 of FIG. 1. The example sampling area generator 102 of FIG.2 may be used to generate sampling areas based on one or more image(s)of one or more geographical area(s) of interest. To generate samplingareas, the example sampling area generator 102 of FIG. 2 includes animage retriever 202, an image divider 204, a zone analyzer 206, a memory208, and a sample stratifier 210.

The example image retriever 202 is coupled to an external network (e.g.,the network 108 of FIG. 1) to retrieve one or more image(s) of thegeographic area(s) of interest. For example, the image retriever 202download one or more images of a geographic area of interest from thegeographic data repository 106. In some examples, the image retriever202 specifies one or more set(s) of coordinates (e.g., globalpositioning system coordinates) defining the geographic area(s) ofinterest. In other examples, the image retriever 202 identifies an imageof the geographic area of interest by identification number, name of thelocation (e.g., city name), and/or any other method of identifyinggeographic areas.

The image retriever 202 of the illustrated example receives (e.g.,downloads) one or more digital images of the geographic area of interestand provides the image(s) to the image divider 204. The receivedimage(s) include sufficient detail to enable measurement of one or moregeographical features of the image. For ease of discussion, this examplewill refer to a single image. However, the teachings of this disclosurealso apply to examples in which multiple whole and/or partial images areprovided for the area of interest. The example image divider 204 of FIG.2 receives the image(s) and divides the image(s) into geographicalzones. In the example of FIG. 2, the image divider 204 determines orcreates the zones to be substantially the same size (e.g., area). Bycreating equally-sized zones, the zones may be efficiently andeffectively compared using geographical features of the zones that maybe readily identifiable via the image. In some examples in which retailstores, product availability, and/or product markets are to be measuredusing the geographical zones, an advantageous sample size is about 0.04square kilometers (e.g., 200 meters by 200 meters, 400 meters by 100meters, etc.). However, different sample sizes and, thus, differentgeographical zone sizes may be advantageous for different applications.

In some examples, the image divider 204 modifies geographical zones tomore closely conform to landmarks and/or geographical locations that areobservable by a person located in the area. Such geographical locationsmay include, for example, intersections and/or addresses of places. Theimage divider 204 may modify the geographical zones to, for example,avoid providing geographical zones having boundaries that are difficultfor a sampler to identify. In some examples, the sizes of the resultingmodified geographical zones are not equal to the sizes of the originalgeographical zones (e.g., substantially uniform zones corresponding to agrid).

As the example image divider 204 of FIG. 2 determines the zones, theimage divider 204 generates a description of each zone to distinguishthat zone from the other zones. For example, the image divider 204 maydescribe each zone via a counter by incrementing the counter for eachzone and assigning the value of the counter to that zone. In otherexamples, the image divider 204 describes each zone using the boundariesof the zone expressed in terms of (1) Global Positioning System (GPS)coordinates (e.g., four points in GPS coordinates for a quadrilateralgeographical zone, identifying the vertices or corners of the zone), (2)Long Range Navigation (LORAN) time difference (TD) information, and/or(3) any other past, present, and/or future positioning system. The imagedivider 204 of the illustrated example stores the descriptions of thegeographical zones into the memory 208 and/or provides the descriptionsof the geographical zones to the zone analyzer 206.

The example zone analyzer 206 of FIG. 2 receives the geographical zonesfrom the image divider 204 and receives the image from the imageretriever 202. Using the image and the geographical zones, the examplezone analyzer 206 measures one or more geographical feature(s) of thegeographic area of interest (e.g., the image) for each of thegeographical zones. For example, the image may show the number and/orsize(s) of buildings within each geographical zone, the roads in each ofthe geographical zones, a number of motor vehicles present (e.g.,on-road and/or off road), and/or some other indicator that ispotentially or actually representative of a population and/or a retailmarket in the geographical zone. The example zone analyzer 206 of FIG. 2measures at least one geographical feature for each of the geographicalzones. In some examples, the zone analyzer 206 measures the samegeographical feature for all of the zones to enable effective comparisonof the values of the zones.

In some examples, the zone analyzer 206 measures the linear length ofroads within each geographical zone and generates a value equal to thetotal linear length of the roads. In some other examples, the zoneanalyzer 206 measures the total area of the roads using the linearlength and a width (e.g., a number of lanes for the roads). Such ameasurement may be useful in locations where wider roads signify moreretail activity and/or, in the case of limited access highways, lessretail activity. The zone analyzer 206 then stores the measured value(s)of the feature(s) of interest for each geographical zone in the memory208 in association with the description of the geographical zone.

The example zone analyzer 206 of FIG. 2 adjusts the measuredgeographical feature(s) for a zone based on the type of geographicalfeature(s) and/or based on the presence of certain characteristics ofthe zone. For example, if a first portion of a zone covers a body ofwater and a second portion of the zone covers land, the zone analyzer206 determines the measured geographical feature(s) (e.g., linear lengthof roads) for the fraction of the second portion. In some examples, thezone analyzer 206 further extrapolates the measurement of thegeographical feature(s) to the geographical zone as though the zone didnot cover water (e.g., only covered land). For example, if 50% of a zonecovered land, the other 50% of the zone covered water, and the measuredlinear length of roads in the zone was 10 kilometers, the zone analyzer206 would determine the measurement for the zone as 20 kilometers.

As used herein, the term “open market” refers to an open-air market,such as a grouping of multiple stalls, kiosks or other small storefronts in a concentrated geographic area. Open markets may be completelycovered, partially covered and partially uncovered, or completelyuncovered. In some examples, the zone analyzer 206 identifies openmarkets (e.g., open air markets) from the image(s) of geographicalareas. To this end, the zone analyzer 206 includes a feature locatorsuch as a scale-invariant feature transform locator 212 (SIFT locator).The SIFT locator 212 is provided with images containing known openmarkets. The example SIFT locator 212 of FIG. 2 identifies features ofthe images corresponding to the known open markets, such as color(s)and/or texture(s) present in the regions of the image(s) including theknown open markets. The example SIFT locator 212 of FIG. 2 then analyzesthe known open markets for patterns or other identifying features of anopen-air market. Such features may be consistent across differentgeographical regions and/or may be characteristic of particulargeographical regions and not other geographical regions. Afteridentifying the features of open markets, the example SIFT locator 212then searches images for additional, unknown or unrecognized openmarkets using the features. The identified open markets may be used, forexample, to generate sampling areas, to locate open markets that are inunenumerated areas (e.g., areas that have not been physically sampledand/or surveyed by a human person or team), to rank open markets basedon criteria (e.g., sizes of the open markets), to predict a number ofstores in an open market, to project growth rate(s), to estimate tradeareas of open markets, and/or to estimate a total number of open marketsin a geographic area. While the example zone analyzer 206 of FIG. 2includes a SIFT locator 212, the example zone analyzer 206 mayadditionally or alternatively use the Bag of Words for Computer Visionmodel, interest point descriptors, and/or any other method of computervision for locating patterns or features.

In some examples, the zone analyzer 206 estimates the demographics(e.g., ages, genders, socioeconomic statuses, etc.) of one or morezone(s) based on the features and measured demographics of one or moreother zones. To this end, the example zone analyzer 206 includes ademographics estimator 214. The demographics estimator 214 receivesdefinition(s) of zone(s) that have been sampled, and the measureddemographics of the sampled zone(s). Using the aerial and/or satelliteimages of the zones, the example demographics estimator 214 determinesgeographic (e.g., artificial and/or natural) features of the zone(s) anddetermines patterns based on the features. The features and/or patternsmay be determined using any appropriate type of computer vision methods,including feature extraction, SIFT, Bag of Words for Computer Vision,interest point descriptors, and/or any other method of computer visionfor locating patterns or features. Using the patterns, the exampledemographics estimator 214 analyzes aerial and/or satellite images ofgeographic areas to be measured to determine (e.g., estimate) thedemographics of the analyzed (e.g., unknown) geographic areas. In thismanner, the demographics estimator 214 provides more reliable estimatesof demographics of geographic areas without the time or costs ofmeasuring those areas.

It can be very costly and sometimes impossible to estimate demographicsthrough traditional methods in the dynamic economies of the developingworld. The example method involves uses satellite imagery and groundtruth (e.g., sampling, measurements based on human observations and/orsurveys) data of sampled areas to generate demographic estimates ofother areas that have not been sampled directly by human surveyors. Themethod may save substantial costs compared with current methods andprovide a method to estimate demographics in areas that that are notsampled directly (e.g., due to impossibility or impracticability).Example approaches disclosed herein use various feature extractionapproaches in the satellite imagery and unique sampling data (e.g.,proprietary sampling data, such as sampling data having been obtained bymanual measurement or sampling).

In some examples, the zone analyzer 206 includes a retail trade area(RTA) identifier 216 to identify retail trade areas based on the aerialand/or satellite images. As used herein, a retail trade area refers to ageographic area or region from which one or more retail locations drawmore than a threshold amount (e.g., a majority) of their customers. Theexample RTA identifier 216 uses feature extraction (e.g., via acomputer) to identify RTAs based on the aerial and/or satellite images.For example, the RTA identifier 216 of FIG. 2 may use the Bag of Wordsfor Computer Vision approach. The example RTA identifier 216 is trainedbased on known sampling data of geographic areas that have been sampled.The RTA identifier 216 may be trained to identify, for example,multimodel demographic and/or building-type information for thegeographic areas that have been sampled. To identify an RTA for ageographic region that has not been sampled (e.g., measured), theexample RTA identifier 216 analyzes a spatial area of a geographicimages for the geographic area to identify features or patterns (e.g.,words in Bag of Words) that are similar to the known geographic areas.In this manner, the example RTA identifier 216 can estimate an RTA ofgeographic areas that are difficult to manually sample or measure for anRTA.

As mentioned above, the example memory 208 of FIG. 2 stores thedescriptions of geographical zones and/or values of feature(s) ofinterest of the zones. For example, the memory 208 stores a record foreach of the geographical zones. A record includes one or more fields forthe description of the corresponding geographical zone and one or morefields to store one or more measured value(s) determined by the zoneanalyzer 206 for corresponding feature(s) of interest. In some examples,the memory 208 stores identifiers for each geographical zone inassociation with the description and/or the value(s). In other examples,the description is used as the identifier of the corresponding zone.

The example sample stratifier 210 of FIG. 2 receives the geographicalzone descriptions and the measured and/or calculated values ofgeographical features of the zones from the memory and/or from the zoneanalyzer 206. Based on the descriptions and the values, the samplestratifier 210 determines a number geographical zones to be sampled. Inthe example of FIG. 2, the sample stratifier 210 determines thegeographical zones to be sampled based on identified open markets and/orinformation following from the identification of open markets (e.g.,open markets located in unenumerated areas, ranks of open markets,predicted numbers of stores in an open market, projected growth rate(s),estimated trade areas of open markets, and/or estimated total numbers ofopen markets in geographic areas). For example, the sample stratifier210 may classify the geographical zones into multiple levels or binsbased on the measured values. The number of geographical zones to besampled in each bin may then be determined based on, for example, thenumbers of geographical zones in each of the bins and/or thecorresponding value(s), range(s) of values, and/or categories of thebin(s). Because the example geographical zones are substantially equalin size, the geographical feature(s) may be compared to estimate apopulation and/or a market size for the corresponding geographicalzones. In some examples, one or more geographical zones have known orclosely approximated characteristics from which the sample stratifier210 estimates populations and/or market sizes of the other zones.

In some examples, the sample stratifier 210 determines the upper andlower limits on the range of values, determines the number of desiredlevels or bins, and determines the bins based on the upper and lowerlimits and the number of desired levels or bins. For example, the samplestratifier 210 of the illustrated example makes each bin an equal rangeof values, a range of percentile values (e.g., the Xth percentile to theYth percentile, the Yth percentile to the Zth percentile, etc.), and/ora predetermined range of values (equal or unequal in size). In someother examples, the sample stratifier 210 uses predetermined and/orstatic bins notwithstanding the upper or lower limits of the range ofvalues in the memory 208. In still other examples, the sample stratifier210 generates bins without a limit on the number of bins, but determinesthe ranges of the bins based on the groupings of values.

In some examples, the sample stratifier 210 generates a mapping of thegeographical zones, the corresponding values, and/or the level or binclassifications. For example, the sample stratifier 210 of theillustrated example generates a heat map illustrating the geographicalzones overlaid on the image of the geographic area of interest, wherethe geographical zones are colored and/or patterned based on the valueand/or the level or bin classification of the corresponding geographicalzone. The pattern and/or the color corresponds to the bin(s) into whichthe value(s) associated with the geographical zone fall. The exampleheat map may advantageously be used to generate a sampling plan,including determining geographical areas are to be sampled.

In some examples, the sampling area generator 102 is used to generateone or more sampling plans having sampling areas. The sampling areas maybe the geographical zones and/or, if appropriate, sub-areas of thegeographical zones. The sampling plan(s) are then used to sample thesampling areas for numbers of retail stores and/or product or brandavailability. The sampling plans may additionally or alternatively beused for other sampling or surveying.

FIGS. 3A-3C illustrate an example satellite image 300 of a geographicarea of interest undergoing an example process to generate samplingareas. For example, the sampling area generator 102 of FIG. 2 maygenerate sampling areas using the example satellite image 300.

FIG. 3A illustrates the example satellite image 300 of a geographic areaof interest. The example geographic area of interest illustrated in FIG.3A is selected as a retail market for which retail statistics about oneor more brands and/or markets are to be measured and/or statisticallysampled. In some other examples, the geographic area of interest isselected to measure and/or statistically sample another type of activity(e.g., media exposure) and/or population.

The example geographic area of interest illustrated in the image 300 ofFIG. 3A includes a number of geographical features 302, 304 that areidentifiable and distinguishable within the image 300. In theillustrated example of FIG. 3A, the geographical features 302, 304include roads within and/or through a geographic area of interest.However, the geographical features 302, 304 may additionally oralternatively include a number of buildings within the geographicalzone, a number of motor vehicles within the geographical zone, waterpresence and area within the geographical zone, a total parks andrecreation land area within the geographical zone, a ratio ofresidential to commercial areas usage within the geographical zone, anindustrial land area within the geographical zone, green area within thegeographical zone, or parking lot area within the geographical zone,and/or some other indicator and/or combination of indicators from thesatellite image and/or any associated geographic data that arepotentially or actually representative of a population, acharacteristic, and/or a retail market in the corresponding geographicalzone. Additionally, the image 300 includes image features such a pixeldensities, pixel intensity across multiple wavelengths, edge, and/orstraight line detectors, which may be used to replace or supplement thegeographical features 302, 304. An example image feature measurement mayinclude determining a density of black pixels (e.g., to measure blackroads), white pixels (e.g., to measure commercial building rooftops),etc., in each geographical zone in the image 300.

FIG. 3B illustrates the example satellite image 300 of FIG. 3A dividedinto geographical zones 306, 308. To more clearly show the examplelayout of the geographical zones 306, 308, the geographical zones 306,308 illustrated in FIG. 3B are considerably larger, in terms of areaencompassed by the geographical zones, than more advantageous sizes forsome applications.

The example satellite image 300 of FIG. 3A may show buildings, roads,waterways, open areas, motor vehicles, and/or other features visible inan aerial and/or satellite photograph. While the image 300 of FIG. 3A isshown in a wider view (e.g., less detail), a more detailed image of aportion (e.g., the geographical zone 310) of the example satellite image300 is shown in FIG. 3D, which illustrates example buildings, roads,waterways, and open areas. The example geographical zones 306, 308 maybe generated by the image divider 204 of FIG. 2. The geographical zones306, 308 may be described with reference to their boundaries, translatedto the imaged area (e.g., the intersection of A Street and B avenue, the1000 block of C Boulevard, Landmark D, etc.), and/or with reference to apositioning system (e.g., GPS, LORAN, etc.). The image divider 204stores descriptions of the geographical zones 306, 308 in the memory 208of FIG. 2.

The example zone analyzer 206 analyzes each of the geographical zones306, 308 (and the other geographical zones illustrated in FIG. 3B) tomeasure the geographical features of interest within the geographicalzones 306, 308 as shown in the image 300. In the illustrated example ofFIGS. 3A-3C, the zone analyzer 206 measures the linear length of theroads present in each geographical zone 306, 308 and generates a totallength value for each geographical zone 306, 308. In other words, thevalue for the geographical zone 306 is the total linear length of theroad(s) and portion(s) of road within the boundaries of the geographicalzone 306. Similarly, the value for the geographical zone 308 is thetotal linear length of the road(s) and portion(s) of road within theboundaries of the geographical zone 308. The zone analyzer 206 storesthe value associated with each geographical zone 306, 308 in the memory208 in association with the description of the geographical zone 306,308.

FIG. 3C illustrates the example satellite image 300 overlaid with theexample geographical zones 306, 308 of FIG. 3B and with indicatorsrepresentative of the value(s) associated with the feature(s) ofinterest in each of the geographical zones 306, 308. In the example ofFIG. 3C, the sampling stratifier 210 of FIG. 2 has applied differentpatterns and/or colors to each of the geographical zones correspondingto the value(s) of the feature(s) of interest stored in the memory 208.For example, the geographical zone 306 has a first linear length ofroad(s) within its boundaries, where the first linear length isclassified in a first bin representative of a relatively low totallength (e.g., bins 1 or 2 out of 7 total bins). In contrast, the examplegeographical zone 308 has a second linear length of road(s) within itsboundaries, where the second linear length is classified in a second binrepresentative of a relatively moderate total length (e.g., bins 3, 4,or 5 out of 7 total bins).

Using the image 300 and the classified geographical zones 306, 308 ofFIG. 3C, the example sample stratifier 210 determines a sampling plan,which determines the number of zones and/or sub-zones that should besampled in each of the bins determined by the sampling stratifier. Forexample, the sample stratifier 210 may determine that more zones shouldbe sampled in the bin represented by the geographical zone 308 than inthe bin represented by the geographical zone 306 because a higher value(e.g., a higher linear length of roads) is present in geographical zoneslike the geographical zone 308 than in geographical zones like thegeographical zone 306. The higher linear length of roads indicates alikely higher population or market in these zones and, thus, the overallsample design can be made more efficient due to statistical efficiencyoccurring via statistical processes such as proportional sampling and/oroptimal sampling within strata. In some examples, the sample stratifier210 further sub-divides some geographical zones to obtain smallersampling areas for ease of data collection by samplers (e.g.,enumerators).

FIG. 3D is an example satellite image of a geographical zone 310selected from the example satellite image of FIG. 3C. As shown in FIG.3D, the image of the geographical zone 310 includes representations ofroads within the geographical zone 310. Based on the density of theroads in the zone 310 (e.g., a relatively high or low density, a densitywithin a particular range of densities, etc.), sample stratifier 210selects the example geographical zone 310 for sampling. The samplestratifier 210 sub-divides the geographical zone 310 to obtain samplingareas that are more easily sampled by samplers (e.g., enumerators).

FIG. 3E is a satellite image of the example geographical zone 310 ofFIG. 3D including sampling areas 312, 314 sub-divided from thegeographical zone 310. In the example of FIG. 3E, the sampling areas312, 314 are selected to be more easily discernible by a sampler ascompared to, for example, the strict borders of the example geographicalzone 310 that may be difficult to discern from the street. As shown inFIG. 3E, some of the example sampling areas 312, 314 extend beyond thegrid structure of FIGS. 3A-3E. Additionally, portions of examplegeographical zone 310 are not covered by a sampling area 312, 314. Insome examples, the non-covered regions of the zone 310 are covered bysampling areas associated with adjacent geographical zones (e.g.,adjacent geographical zones 316, 318). In the example of FIG. 3E, thesampling stratifier 210 selects the illustrated sampling areas to coverapproximately the same amount of geographic area as the geographicalzone 310.

The example sampling stratifier 210 of FIG. 2 generates the examplesampling areas 312, 314 based on an identification of open markets 320,322 in the example areas 312, 314. For example, the sampling stratifier210 generates the sampling areas 312, 314 to include identified openmarkets that may be representative of market channels, demography,and/or other measurable market characteristics of the areas 312, 314.

While example manners of implementing the sampling area generator havebeen illustrated in FIG. 2, one or more of the elements, processesand/or devices illustrated in FIG. 2 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example image retriever 202, the example image divider 204,the example zone analyzer 206, the example memory 208, the examplesample stratifier 210, the example SIFT locator 212, the exampledemographics estimator 214, the example RTA identifier 216 and/or, moregenerally, the example sampling area generator 102 of FIG. 2 may beimplemented by hardware, software, firmware and/or any combination ofhardware, software and/or firmware. Thus, for example, any of theexample image retriever 202, the example image divider 204, the examplezone analyzer 206, the example memory 208, the example sample stratifier210, the example SIFT locator 212, the example demographics estimator214, the example RTA identifier 216 and/or, more generally, the examplesampling area generator 102 of FIG. 2 could be implemented by one ormore circuit(s), programmable processor(s), application specificintegrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s))and/or field programmable logic device(s) (FPLD(s)), etc. When any ofthe appended apparatus or system claims are read to cover a purelysoftware and/or firmware implementation, at least one of the exampleimage retriever 202, the example image divider 204, the example zoneanalyzer 206, the example memory 208, the example sample stratifier 210,the example SIFT locator 212, the example demographics estimator 214,and/or the example RTA identifier 216 are hereby expressly defined toinclude a tangible computer readable medium such as a memory, DVD, CD,etc. storing the software and/or firmware. Further still, the examplesampling area generator 102 of FIG. 2 may include one or more elements,processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 2, and/or may include more than one of any or all ofthe illustrated elements, processes and devices.

Flowcharts representative of example machine readable instructions forimplementing the sampling area generator 102 of FIG. 2 are shown inFIGS. 4-14. In these examples, the machine readable instructionscomprise program(s) for execution by a processor such as the processor1512 shown in the example processor platform 1500 discussed below inconnection with FIG. 15. The program may be embodied in software storedon a tangible computer readable medium such as a CD-ROM, a floppy disk,a hard drive, a digital versatile disk (DVD), or a memory associatedwith the processor 1512, but the entire program and/or parts thereofcould alternatively be executed by a device other than the processor1512 and/or embodied in firmware or dedicated hardware. Further,although the example program(s) are described with reference to theflowcharts illustrated in FIGS. 4-14, many other methods of implementingthe sampling area generator 102 may alternatively be used. For example,the order of execution of the blocks may be changed, and/or some of theblocks described may be changed, eliminated, or combined.

As mentioned above, the example processes of FIGS. 4-14 may beimplemented using coded instructions (e.g., computer readableinstructions) stored on a tangible computer readable medium such as ahard disk drive, a flash memory, a read-only memory (ROM), a compactdisk (CD), a digital versatile disk (DVD), a cache, a random-accessmemory (RAM) and/or any other storage media in which information isstored for any duration (e.g., for extended time periods, permanently,brief instances, for temporarily buffering, and/or for caching of theinformation). As used herein, the term tangible computer readable mediumis expressly defined to include any type of computer readable storageand to exclude propagating signals. Additionally or alternatively, theexample processes of FIGS. 4-14 may be implemented using codedinstructions (e.g., computer readable instructions) stored on anon-transitory computer readable medium such as a hard disk drive, aflash memory, a read-only memory, a compact disk, a digital versatiledisk, a cache, a random-access memory and/or any other storage media inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, brief instances, for temporarily buffering, and/orfor caching of the information). As used herein, the term non-transitorycomputer readable medium is expressly defined to include any type ofcomputer readable medium and to exclude propagating signals.

FIG. 4 is a flowchart representative of example machine readableinstructions 400 which may be executed to implement the example samplingarea generator 102 of FIGS. 1 and 2 to measure one or more geographicalfeature(s) present in an image of a geographical location.

The example instructions 400 begin by receiving an image of ageographical area of interest (block 402). For example, the imageretriever 202 of FIG. 2 may request and receive an image (e.g., theimage 300 of FIG. 3A) via the network 108 of FIG. 1. In the example ofFIG. 4, the image is sufficiently detailed to distinguish geographicalfeatures of interest in the region. The image divider 204 of FIG. 2divides the image into geographical zones which have equal orapproximately equal physical areas (block 404). For example, the imagedivider 204 divides the image 300 into geographical zones 306, 308.

The example image divider 204 modifies the geographical zones (block405). For example, the image divider 204 may modify one or more of thegeographical zones to more closely conform to landmarks and/orgeographical locations that are observable by a person located in thearea. Such geographical locations may include, for example,intersections and/or addresses of places. The image divider 204 storesdescriptions of the geographical zones 306, 308 into the example memory208 of FIG. 2 (block 406).

The example instructions 400 enter a loop 408 to process each of theexample geographical zones 306, 308 in the memory 208. For thegeographical zone 306 of FIG. 3B, the zone analyzer 206 measuresgeographical feature(s) and/or image feature(s) of the geographical zone306 (block 410). In the example of FIGS. 3B and 3C, the zone analyzer206 measures the total linear length of roads and portion(s) of roadsthat lie within the geographical zone 306. In some examples, the zoneanalyzer 206 extrapolates the measured value(s) based on acharacteristic or feature of the geographical zone 306. The zoneanalyzer 206 stores value(s) representative of the measured geographicalfeature(s) and/or image feature(s) in the memory 208 with thedescription of the corresponding geographical zone 306 (block 412). Forexample, the zone analyzer 206 may store the total linear road lengthfor the geographical zone 306 in a record in memory 208 containing thedescription of the geographical zone 306. After block 412, the exampleinstructions 400 iterate the loop 408 (e.g., blocks 410 and 412) for thenext geographical zone (e.g., the geographical zone 308) until all or adesired number of the geographical zones have been processed by the loop408.

After processing the example geographical zones, the example samplingstratifier 210 of FIG. 2 classifies the geographical zones 306, 308based on the values stored with the zones (block 414). For example, thesampling stratifier 210 receives records from the memory 208, includingthe descriptions and values for each of the geographical zones 306, 308,and determines levels or bins into which the values may fall. In someexamples, the sample stratifier 210 determines the upper and lowerlimits on the range of values, determines the number of desired levelsor bins, and determines the bins based on the upper and lower limits andthe number of desired levels or bins. For example, the sample stratifier210 may make each bin an equal range of values, a range of percentilevalues (e.g., the Xth percentile to the Yth percentile, the Ythpercentile to the Zth percentile, etc.), a predetermined range of values(equal or unequal in size), and/or any other range or division. In someother examples, the sample stratifier 210 uses predetermined and/orstatic bins notwithstanding the upper or lower limits of the range ofvalues in the memory 208.

The example sample stratifier 210 generates a map of geographical zonesbased on the classification (block 416). An example map generated by thesample stratifier 210 is a heat map showing each geographical zone(which may be laid over the image), each geographical zone including apattern and/or a color. The pattern and/or the color corresponds to thebin(s) into which the value(s) associated with the geographical zonefalls. As illustrated in the example heat map of FIG. 3C, thegeographical zones 306, 308 include respective patterns to visuallyillustrate the value range of the corresponding geographical zones 306,308.

FIG. 5 is a flowchart representative of example machine readableinstructions 500 which may be executed to implement the example samplingarea generator 102 of FIGS. 1 and 2 to generate areas to be sampled.

The example instructions 500 of FIG. 5 begin by receiving (e.g., at thesampling stratifier 210 of FIG. 2) geographical zone descriptions andvalue(s) stored with the descriptions (block 502). For example, thesampling stratifier 210 may receive the zone descriptions and value(s)from the image divider 204, the zone analyzer 206, the memory 208,and/or a combination thereof. Based on the values, the example samplingstratifier 210 places the geographical zones into respective bins (block504). For example, the sample stratifier 210 may determine upper andlower limits on a range of values for each bin, determine the number ofdesired levels or bins, and determine the bins based on the upper andlower limits and the number of desired levels or bins.

The example sample stratifier 210 determines the number(s) ofgeographical zone(s) to be sampled from each bin (block 506). Thenumbers of zones to be sampled from a bin may be based on, for example,the number of zones in the bin and/or the ranges of values for the bin.

The example instructions 500 then enter a loop 508 for each of thereceived geographical zones. For example, the sampling stratifier 210may perform the loop 504 for each of the geographical zones received inblock 502.

For the example geographical zone 306 of FIG. 3B, the samplingstratifier 210 determines whether to use the geographical zone 306 as asampling area (block 510). The determination may be made based on, forexample, whether a threshold number of geographical zones have beenselected for sampling from the same bin as the geographical zone underconsideration, other geographic criteria that make the geographical zonerepresentative of one or more other geographical zone(s), and/or arandom or pseudorandom factor.

If the geographical zone is to be used as a sampling area (block 510),the example sampling stratifier 210 whether the geographical zone 306 issufficiently-highly populated and/or geographically large enough tosub-divide the geographical zone 306 (block 512). Sub-dividing may beperformed if, for example, sampling the entire geographical zone 306would be substantially inconvenient or impractical for the sampler. Ifthe sampling stratifier 210 determines that geographical zone 306 is tobe subdivided (block 512), the example sampling stratifier 210 generatessub-sampling area(s) within the geographical zone 306 (block 514). Whileeach of the example sub-sampling areas is independently sampled, thesub-sampling areas are considered to be within the geographical zone306. The number of sampling area(s) in the geographical zone 306 may bebased on the value associated with the geographical zone 306. Generallyspeaking, a higher value will result in more sub-sampling areas beinggenerated within the geographical zone 306.

In some examples, the sampling stratifier 210 generates a number ofsub-sampling areas such that the value divided by the number of samplingareas yields a quotient less than the threshold. In some other examples,the sampling stratifier 210 repeatedly divides the geographical zonesinto sub-sampling areas, sub-sampling areas into sub-sub-sampling areas,etc., until each of the sub-sampling area(s), sub-sub-sampling area(s),etc., having a value less than the threshold. To this end, the examplesample stratifier 210 iterates with the example zone analyzer 206 tomeasure geographical feature(s) of the sub-sampling areas, thesub-sub-sampling areas, etc., and then compare(s) the value(s)determined by the measurement to the threshold.

After subdividing the geographical zone (block 514), if the samplingstratifier 210 determines not to sub-divide the geographical zone (block512), or the geographical zone is not to be used as a sampling area(block 510), the example sampling stratifier 210 iterates the loop 504for the next geographical zone.

When the geographical zones have been processed via the loop 508, theexample sampling stratifier 210 sends the sampling areas to a sampler(block 516). For example, the sampling areas may be sent to the samplerin the form of a map including the boundaries of the sampling areas. Thesampler may be another entity responsible for physically sampling thesampling areas such as, for example, a retail enumeration service in thecase of sampling retail markets and/or brands. In some examples, thesampler decides and/or reviews the sampling areas to determine adifferent set of sampling areas from the geographical zones.

After sending the sampling areas (block 516), the example instructions500 may end. Alternatively, the instructions 500 may iterate for anotherset of geographical zone descriptions (e.g., another geographic area ofinterest).

From the foregoing, it will be appreciated that methods, apparatusand/or articles of manufacture disclosed herein may be used to measuregeographical feature(s) of a geographic area of interest. Examplemethods, apparatus, and/or articles of manufacture advantageouslyimprove accuracy in retail sampling, thereby improving data quality andcoverage. Additionally, example methods, apparatus, and/or articles ofmanufacture disclosed herein standardize sampling and reporting ofretail categories, thereby reducing variance in statistical sampling andimproving overall statistical quality. By covering more of thegeographical area of interest, example methods, apparatus, and/orarticles of manufacture disclosed herein can provide more accurateretail data for retail markets. Disclosed example methods, apparatus,and/or articles of manufacture are able to more rapidly and completelysample a market than known retail sampling techniques, and rapidlydeliver measurement of newly-developed markets or market areas within ageographic area of interest. Additionally, example methods, apparatus,and/or articles of manufacture disclosed herein can provide clients morecurrent and/or relevant retail data, to better inform the client'smarketing decisions as to the area of interest.

FIG. 6 is a flowchart representative of example machine readableinstructions 600 which may be executed to implement the example zoneanalyzer 206 of FIG. 2 to locate open markets.

The example instructions 600 begin by obtaining (e.g., at the zoneanalyzer 206 of FIG. 2) aerial and/or satellite image(s) of area(s)having known open markets (block 602). The example zone analyzer 206also obtains aerial and/or satellite images of geographic areas to bemeasured (block 604). The images obtained in blocks 602 and 604 mayoverlap, may be identical, and/or may be different. The example zoneanalyzer 206 then locates known open markets in the images (block 606).For example, the zone analyzer 206 may be provided with coordinates ofknown open markets, which the zone analyzer 206 matches to areas in theimages.

The example zone analyzer 206 analyzes features of the known marketsfrom the images to locate additional open markets (e.g., from the imagesof areas to be measured) (block 608). Block 608 may be implemented usingthe example instructions described below with reference to FIG. 7. Theexample zone analyzer 206 receives a list or set of located openmarkets, and returns the located open markets to a calling function. Theexample instructions 600 may then end and/or iterate to locateadditional open markets.

FIG. 7 is a flowchart representative of example machine readableinstructions 700 which may be executed to implement the example zoneanalyzer 206 and/or the SIFT locator 212 of FIG. 2 to analyze featuresof an image of a geographical location. The instructions 700 may beimplemented by the example zone analyzer 206 in response to receiving aset of known open markets, a set of images including the known openmarkets, and a set of images of geographic areas to be measured (e.g.,from which open markets are to be located).

The example zone analyzer 206 enters a for-loop 702 for each of theknown open markets. For each of the known open markets, the example zoneanalyzer 206 determines features, such as textures and/or colors, of theimage area for the known open market (block 704). When each of the knownopen markets has been processed through the loop 702, the example zoneanalyzer trains a scale-invariant feature transform locator (e.g., theSIFT locator 212 of FIG. 2) using the images and features. The exampleSIFT locator 212 obtains SIFT features from image(s) to be measured(block 708).

The example SIFT locator 212 executes a for-loop 710 for each of theSIFT features from the images to be measured. For each SIFT feature, theexample SIFT locator 212 determines whether the SIFT feature matches anySIFT features of known open market(s) (and/or SIFT features derived frommultiple known open markets) (block 712). If the SIFT feature matches(block 712), the example SIFT locator 212 marks the area of the imagecontaining the SIFT feature as a possible open market (block 714). Onthe other hand, if the SIFT feature does not match any of the SIFTfeatures (block 712), the example SIFT locator 212 discards the SIFTfeature (block 716). After looping through the set of SIFT features, theexample instructions 700 of FIG. 7 end and the SIFT locator 212 returnsthe possible open market(s). For instance, control returns to theinstructions 600 of FIG. 6 and/or to a calling function.

FIG. 8 is a flowchart representative of example machine readableinstructions 800 which may be executed to implement the example zoneanalyzer 206 of FIG. 2 to use located open markets to locate openmarkets that are in unenumerated areas (e.g., areas that have not beenphysically sampled or surveyed for open markets). The instructions ofFIG. 8 may be performed using satellite and/or aerial images of one ormore geographic regions.

The example instructions 800 of FIG. 8 begin by locating (e.g., via thezone analyzer 206) open markets (block 802). The example block 802 maybe performed using the instructions 600, 700 of FIGS. 6 and/or 7. Block802 results in a set of one or more open markets, their locations,and/or other characteristic information based on the satellite and/oraerial images.

Based on the located open markets, the example zone analyzer 206 locatesones of the located open markets that are in unenumerated areas (block804). For example, the zone analyzer 206 may report those located openmarkets that were identified from images of geographical areas that havenot previously been sampled (e.g., by identifying which located openmarkets located within processed images of unenumerated areas). In otherwords, the example zone analyzer 206 of FIG. 2 determines which of thelocated open markets are in enumerated areas (e.g., previously sampledor surveyed areas) and which of the located open markets are withinunenumerated areas. The reported located open markets may beadvantageously used to target new open markets in unenumerated areas formeasurements, sales efforts in unenumerated areas, and/or any otherpurpose. In some other examples, the zone analyzer 206 may report thoselocated open markets that were identified from images of geographicalareas that have been previously sampled or where nearby areas have beensampled. The example instructions 800 of FIG. 8 then end.

FIG. 9 is a flowchart representative of example machine readableinstructions 900 which may be executed to implement the example zoneanalyzer 206 of FIG. 2 to use data representative of located openmarkets to rank open markets. The instructions of FIG. 9 may beperformed using satellite and/or aerial images of one or more geographicregions.

The example instructions 900 of FIG. 9 begin by locating (e.g., via thezone analyzer 206) open markets (block 902). The example block 902 maybe performed using the instructions 600, 700 of FIGS. 6 and/or 7. Block902 results in a set of one or more open markets, their locations,and/or other characteristic information based on the satellite and/oraerial images.

Based on the located open markets, the example zone analyzer 206 createsa ranked list of the located open markets based on size (block 904). Forexample, the zone analyzer 206 may determine the size of the image areawithin which the located open market was located, and translate the sizeof the image area to a physical area (e.g., using a scale of the imageto actual distances). The located markets are then included in theranked list in ascending or descending order of physical area. Thisinformation may be advantageously used to target certain open marketsbefore others. Other criteria for ranking open markets may additionallyor alternatively be used. The example instructions 900 of FIG. 9 thenend.

FIG. 10 is a flowchart representative of example machine readableinstructions 1000 which may be executed to implement the example zoneanalyzer 206 of FIG. 2 to use data representative of located openmarkets to predict a number of stores in an open market. Theinstructions of FIG. 10 may be performed using satellite and/or aerialimages of one or more geographic regions.

The example instructions 1000 of FIG. 10 begin by locating open markets(e.g., via the zone analyzer 206) (block 1002). The example block 1002may be performed using the instructions 600, 700 of FIGS. 6 and/or 7.Block 1002 results in a set of one or more open markets, theirlocations, and/or other characteristic information based on thesatellite and/or aerial images.

Based on the located open markets, the example zone analyzer 206predicts a number of stores in the located open markets based on an areain the image (block 1004). For example, the zone analyzer 206 maypredict the number of stores in the located open market based on thearea of the open market, densities of similar open markets, a number ofstores identifiable in the image, a number and/or areas of walkingpathways within the image, and/or from other features. In some examples,the prediction of a number of stores is based on the similarities and/ordifferences of features between an open market being predicted and knownmarket(s). This information may be advantageously used to accuratelyestimate store and/or product densities without the expensive andtime-consuming process of manually sampling or measuring the locatedopen markets. The example instructions 1000 of FIG. 10 then end.

FIG. 11 is a flowchart representative of example machine readableinstructions 1100 which may be executed to implement the example zoneanalyzer 206 of FIG. 2 to use data representative of located openmarkets to project a growth rate of an open market. The instructions ofFIG. 11 may be performed using satellite and/or aerial images of one ormore geographic regions.

The example instructions 1100 of FIG. 11 begin by locating open markets(e.g., via the zone analyzer 206) (block 1102). The example block 1102may be performed using the instructions 600, 700 of FIGS. 6 and/or 7.Block 1102 results in a set of one or more open markets, theirlocations, and/or other characteristic information based on thesatellite and/or aerial images.

Based on the located open markets, the example zone analyzer 206projects growth rate(s) of located open market(s) over time (block1104). For example, the zone analyzer 206 may project a growth rate of alocated open market based on the size of the market, a building and/orpopulation density of a surrounding area, known growth rate(s) ofsimilar open markets that have been sampled, and/or any other factors.In some examples, the zone analyzer 206 monitors the open market atmultiple times to identify an estimated growth rate based on past growthrates. This information may be advantageously used to generate a planfor targeting particular open markets at particular times or within timeranges. The example instructions 1100 of FIG. 11 then end.

FIG. 12 is a flowchart representative of example machine readableinstructions 1200 which may be executed to implement the example zoneanalyzer 206 of FIG. 2 to use located open markets to modify samplingareas. The instructions of FIG. 12 may be performed using satelliteand/or aerial images of one or more geographic regions.

The example instructions 1200 of FIG. 12 begin by locating (e.g., viathe zone analyzer 206) open markets (block 1202). The example block 1202may be performed using the instructions 600, 700 of FIGS. 6 and/or 7.Block 1202 results in a set of one or more open markets, theirlocations, and/or other characteristic information based on thesatellite and/or aerial images.

Based on the located open markets, the example sample stratifier 210modifies one or more sampling areas to include a representative numberof located open markets (block 1204). For example, the sample stratifier210 may take located open markets and known numbers and/or rates of openmarkets in sampled areas into consideration to generate additionalsampling areas. The example sample stratifier 210 may additionally oralternatively use the located open markets to modify the zones, asdescribed above, to achieve a representative sampling area. Thisinformation may be advantageously used to improve sampling quality. Theexample instructions 1200 of FIG. 12 then end.

FIG. 13 is a flowchart representative of example machine readableinstructions 1300 which may be executed to implement the example zoneanalyzer 206 of FIG. 2 to use data representative of located openmarkets to estimate a total number of open markets in a geographic area.The instructions of FIG. 13 may be performed using satellite and/oraerial images of one or more geographic regions.

The example instructions 1300 of FIG. 13 begin by locating open markets(e.g., via the zone analyzer 206) (block 1302). The example block 1302may be performed using the instructions 600, 700 of FIGS. 6 and/or 7.Block 1302 results in a set of one or more open markets, theirlocations, and/or other characteristic information based on thesatellite and/or aerial images.

Based on the located open markets, the example zone analyzer 206estimates a total number of open markets in geographic area(s) based onthe located open markets (block 1304). For example, the zone analyzer206 may make an estimate for a geographic area based on another,comparable geographic area. The example zone analyzer 206 mayadditionally or alternatively estimate a number of open markets in ageographic area based on known markets in the area, located open marketsin the area, a computed density of open markets in the area, and a sizeof the area. This information may be advantageously used to improve openmarket estimation and/or improve sampling quality. The exampleinstructions 1300 of FIG. 13 then end.

FIG. 14 is a flowchart representative of example machine readableinstructions 1400 which may be executed to implement the example zoneanalyzer 206 of FIG. 2 to use located open markets to estimate a tradearea of an open market. The instructions of FIG. 14 may be performedusing satellite and/or aerial images of one or more geographic regions.

The example instructions 1400 of FIG. 14 begin by locating (e.g., viathe zone analyzer 206) open markets (block 1402). The example block 1402may be performed using the instructions 600, 700 of FIGS. 6 and/or 7.Block 1402 results in a set of one or more open markets, theirlocations, and/or other characteristic information based on thesatellite and/or aerial images.

Based on the located open markets, the example zone analyzer 206estimates the trade areas of the located open markets (block 1404). Forexample, the zone analyzer 206 may determine the geographic area that isserved and/or affected by a particular known open market and/or aparticular located open market. To determine the trade area, the examplezone analyzer 206 may use determined (e.g., estimated) trade areas ofknown open markets, the surrounding features and/or demographics of theknown open markets, and the surrounding features and/or demographics ofthe located open market. The determined trade area may be used to, forexample, concentrate on particular ones of the located open marketsbased on the trade area. The example instructions 1400 of FIG. 14 thenend.

FIG. 15 is a block diagram of an example processor platform 1500 capableof executing the instructions of FIGS. 4-14 to implement the system 100and/or the sampling area generator 102 of FIGS. 1 and/or 2. Theprocessor platform 1500 can be, for example, a server, a personalcomputer, or any other type of computing device.

The processor platform 1500 of the illustrated example includes aprocessor 1512. The processor 1512 of the illustrated example ishardware. For example, the processor 1512 can be implemented by one ormore integrated circuits, logic circuits, microprocessors or controllersfrom any desired family or manufacturer.

The processor 1512 of the illustrated example includes a local memory1513 (e.g., a cache). The processor 1512 of the illustrated example isin communication with a main memory including a volatile memory 1514 anda non-volatile memory 1516 via a bus 1518. The volatile memory 1514 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory(RDRAM) and/or any other type of random access memory device. Thenon-volatile memory 1516 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 1514,1516 is controlled by a memory controller.

The processor platform 1500 of the illustrated example also includes aninterface circuit 1520. The interface circuit 1520 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 1522 are connectedto the interface circuit 1520. The input device(s) 1522 permit(s) a userto enter data and commands into the processor 1512. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 1524 are also connected to the interfacecircuit 1520 of the illustrated example. The output devices 1524 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a light emitting diode (LED), a printer and/or speakers).The interface circuit 1520 of the illustrated example, thus, typicallyincludes a graphics driver card, a graphics driver chip or a graphicsdriver processor.

The interface circuit 1520 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network1526 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 1500 of the illustrated example also includes oneor more mass storage devices 1528 for storing software and/or data.Examples of such mass storage devices 1528 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 1532 of FIGS. 4-14 may be stored in the massstorage device 1528, in the volatile memory 1514, in the non-volatilememory 1516, and/or on a removable tangible computer readable storagemedium such as a CD or DVD.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. A method, comprising: identifying, by executingan instruction with a processor, first features of a first open marketfrom a first image of a first geographic area; identifying, by executingan instruction with the processor, second features of a second image ofa second geographic area; comparing the first features to the secondfeatures by executing an instruction with the processor; identifying, byexecuting an instruction with the processor, additional open markets inthe second image based on the comparison; determining, by executing aninstruction with the processor, first characteristics of the additionalopen markets based on the comparison of the first features to the secondfeatures and corresponding second characteristics of the first openmarket, the first characteristics including at least one of a) numbersof stores in the additional open markets, b) physical sizes of theadditional open markets, c) whether the additional open markets are inpreviously enumerated areas or previously unenumerated areas, or d)trade areas of the additional open markets; modifying, by executing aninstruction with the processor, a sampling area in the second geographicarea to be physically sampled to include in the sampling area at least asubset of the additional open markets in the second geographic areabased on the first characteristics; and providing the modified samplingarea to a sampler for physical sampling of the second geographic areaaccording to the modified sampling area to determine a number of storesin the second geographic area, the physical sampling includingdetermining a retail characteristic of the modified sampling area. 2.The method of claim 1, further including training a feature locatorusing the first features and the first image.
 3. The method of claim 1,wherein identifying the first features includes using a scale-invariantfeature transform, a Bag of Words for Computer Vision model, or interestpoint descriptors.
 4. The method of claim 1, further includingidentifying a trade area for a first one of the additional open markets,identifying the trade area including determining the trade area to haveat least a threshold number of customers of the first one of theadditional open markets.
 5. The method of claim 1, further includingpredicting a first number of stores in a first one of the additionalopen markets based on an area of at least a portion of the second image.6. The method of claim 1, further including ranking the additional openmarkets relative to a third open market in a third image.
 7. The methodof claim 1, wherein the first and second images include aerial images.8. A method of claim 1, further including estimating a growth rate of afirst one of the additional open markets based on the first features. 9.An apparatus, comprising: an image divider to define a first image of afirst geographic area and a second image of a second geographic area; afeature locator to identify first features of a first open market fromthe first image and to identify second features of a portion of thesecond image; a zone analyzer to: compare the first features to thesecond features and identify additional open markets in the portion ofthe second image based on the comparison; and determine firstcharacteristics of the additional open markets based on the comparisonof the first features to the second features and corresponding secondcharacteristics of the first open market, the first characteristicsincluding at least one of a) numbers of stores in the additional openmarkets, b) physical sizes of the additional open markets, c) whetherthe additional open markets are in previously enumerated areas orpreviously unenumerated areas, or d) trade areas of the additional openmarkets; and a sample stratifier to: modify a sampling area in thesecond geographic area to be physically sampled to include in thesampling area at least a subset of the additional open markets in thesecond geographic area based on the first characteristics; and providethe modified sampling area to a sampler for physical sampling of thesecond geographic area according to the modified sampling area todetermine a number of stores in the second geographic area, the physicalsampling including determining a retail characteristic of the modifiedsampling area.
 10. The apparatus of claim 9, further including a retailtrade area identifier to identify a retail trade area of a first one ofthe additional open markets, the retail trade area including at least athreshold amount of customers of the -a first one of the additional openmarkets.
 11. The apparatus of claim 9, wherein the feature locatorincludes a scale invariant feature transform locator to identify thefirst features using a scale invariant feature transform.
 12. Theapparatus of claim 9, wherein the zone analyzer is to rank the firstopen market relative to the additional open markets.
 13. The apparatusof claim 9, wherein the zone analyzer is to estimate the numbers ofstores in the additional open markets of stores in the additional openmarkets.
 14. The apparatus of claim 9, wherein the zone analyzer is toestimate a total number of open markets in the second geographic area.15. A tangible computer readable medium comprising computer readableinstructions which, when executed, cause a processor to at least:identify first features of a first open market from a first image of afirst geographic area; identify second features of a second image of asecond geographic area; compare the first features to the secondfeatures; identify additional open markets in the second image based onthe comparisons; determine first characteristics of the additional openmarkets based on the comparison of the first features to the secondfeatures and corresponding second characteristics of the first openmarket, the first characteristics including at least one of a) numbersof stores in the additional open markets, b) physical sizes of theadditional open markets, c) whether the additional open markets are inpreviously enumerated areas or previously unenumerated areas, or d)trade areas of the additional open markets; modify a sampling area inthe second geographic area to be physically sampled to include in thesampling area at least a subset of the additional open markets in thesecond geographic area based on the first characteristics; and providethe modified sampling area to a sampler for physical sampling of thesecond geographic area according to the modified sampling area todetermine a number of stores in the second geographic area, the physicalsampling including determining a retail characteristic of the modifiedsampling area.
 16. The tangible computer readable medium of claim 15,wherein the instructions are further to cause the processor to train afeature locator using the first features and the first image.
 17. Thetangible computer readable medium of claim 15, wherein the instructionsare to cause the processor to identify the first features using ascale-invariant feature transform, a Bag of Words for Computer Visionmodel, or interest point descriptors.
 18. The tangible computer readablemedium of claim 15, wherein the instructions are further to cause theprocessor to identify the trade areas of the additional open marketsbased on physical distances between the first and additional openmarkets.
 19. The tangible computer readable medium of claim 15, whereinthe instructions are further to cause the processor to estimate a totalnumber of open markets in the second geographic area based on locatedopen markets in the second geographic area, the located open marketsincluding the additional open markets.